Paper 2016/314

Blind Source Separation from Single Measurements using Singular Spectrum Analysis

Santos Merino Del Pozo and François-Xavier Standaert

Abstract

Singular Spectrum Analysis (SSA) is a powerful data decomposition/recomposition technique that can be used to reduce the noise in time series. Compared to existing solutions aiming at similar purposes, such as frequency-based filtering, it benefits from easier-to-exploit intuitions, applicability in contexts where low sampling rates make standard frequency analyses challenging, and the (theoretical) possibility to separate a signal source from a noisy source even if both run at the same frequency. In this paper, we first describe how to apply SSA in the context of side-channel analysis, and then validate its interest in three different scenarios. Namely, we consider unprotected software, masked software, and unprotected hardware block cipher implementations. Our experiments confirm significant noise reductions in all three cases, leading to success rates improved accordingly. They also put forward the stronger impact of SSA in more challenging scenarios, e.g. masked implementations (because the impact of noise increases exponentially with the number of shares in this case), or noisy hardware implementations (because of the established connection between the amount of noise and the attacks' success rate in this case). Since noise is a fundamental ingredient for most countermeasures against side-channel attacks, we conclude SSA can be an important element in the toolbox of evaluation laboratories, in order to efficiently preprocess their measurements in a black box manner.

Metadata
Available format(s)
PDF
Category
Implementation
Publication info
Published by the IACR in CHES 2015
DOI
10.1007/978-3-662-48324-4_3
Keywords
side-channel analysissignal processingfilteringsingular spectrum analysis
Contact author(s)
santos merino @ uclouvain be
History
2016-03-21: received
Short URL
https://ia.cr/2016/314
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2016/314,
      author = {Santos Merino Del Pozo and François-Xavier Standaert},
      title = {Blind Source Separation from Single Measurements using Singular Spectrum Analysis},
      howpublished = {Cryptology ePrint Archive, Paper 2016/314},
      year = {2016},
      doi = {10.1007/978-3-662-48324-4_3},
      note = {\url{https://eprint.iacr.org/2016/314}},
      url = {https://eprint.iacr.org/2016/314}
}
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